{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "691f6bb9",
   "metadata": {},
   "outputs": [],
   "source": [
    "import csv\n",
    "import matplotlib.pyplot as plt  # 导入pyplot模块\n",
    "plt.rcParams['font.sans-serif'] = ['SimHei']  # 解决中文乱码问题\n",
    "plt.rcParams['axes.unicode_minus'] = False  # 解决正负号乱码问题"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "6ef9e5dc",
   "metadata": {},
   "outputs": [],
   "source": [
    "fields1 = []\n",
    "data1 = []\n",
    "fields2 = []\n",
    "data2 = []\n",
    "failure_count = [0,0,0,0,0,0]\n",
    "all_count = [0,0,0,0,0,0]\n",
    "failure_percen_list = [0.0,0.0,0.0,0.0,0.0,0.0]\n",
    "fnum = 0\n",
    "anum = 0"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "a69c23d1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "18387\n",
      "[3194, 7437, 4539, 1329, 1601, 274]\n"
     ]
    }
   ],
   "source": [
    "f1 = open('./data/ssd_failure_tag.csv','r')\n",
    "failure = csv.reader(f1,delimiter=',')\n",
    "fields1 = failure.__next__()\n",
    "for row in failure:\n",
    "    data1.append(row)\n",
    "#    print(row)\n",
    "    fnum = fnum + 1\n",
    "print(fnum)\n",
    "for i in range(0,fnum,1):\n",
    "    if data1[i][28] == '':\n",
    "        continue\n",
    "    data1[i][28] = data1[i][28].strip()\n",
    "    data1[i][28] = data1[i][28].strip(\"\\n\")\n",
    "    data1[i][28] = data1[i][28].strip(\"\\t\")\n",
    "    data1[i][28] = data1[i][28].strip(\"[\")\n",
    "    data1[i][28] = data1[i][28].strip(\"]\")\n",
    "    y1 = int(int(float(data1[i][28]))/(24*365))\n",
    "    if y1 >= 6:\n",
    "        continue\n",
    "#    print(y)\n",
    "    failure_count[y1]=failure_count[y1]+1\n",
    "print(failure_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "a0dfc91f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "706182\n",
      "[12854, 87318, 155834, 172414, 181581, 91387]\n"
     ]
    }
   ],
   "source": [
    "f2 = open('./data/20191231.csv','r')\n",
    "day_data = csv.reader(f2,delimiter=',')\n",
    "fields2 = day_data.__next__()\n",
    "for row in day_data:\n",
    "    data2.append(row)\n",
    "#    print(row)\n",
    "    anum = anum + 1\n",
    "print(anum)\n",
    "for j in range(0,anum,1):\n",
    "    if data2[j][20] == '':\n",
    "        continue\n",
    "    data2[j][20] = data2[j][20].strip()\n",
    "    data2[j][20] = data2[j][20].strip(\"\\n\")\n",
    "    data2[j][20] = data2[j][20].strip(\"\\t\")\n",
    "    data2[j][20] = data2[j][20].strip(\"[\")\n",
    "    data2[j][20] = data2[j][20].strip(\"]\")\n",
    "    y2 = int(int(float(data2[j][20]))/(24*365))\n",
    "    if y2 >= 6:\n",
    "        continue\n",
    "    all_count[y2]=all_count[y2]+1\n",
    "print(all_count)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "c17f6c2e",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "2.6037197209784444\n",
      "[24.848296250194494, 8.517144231429945, 2.9127148119152433, 0.77081907501711, 0.8817001778820471, 0.29982382614595077]\n"
     ]
    }
   ],
   "source": [
    "all_failure_percen = (fnum/anum)*100\n",
    "for i in range(0,6,1):\n",
    "    t = failure_count[i]/all_count[i]\n",
    "#    print(t)\n",
    "    failure_percen_list[i] = t\n",
    "\n",
    "\n",
    "for i in range(0,6,1):\n",
    "#    print(t)\n",
    "    failure_percen_list[i] = failure_percen_list[i]*100\n",
    "print(all_failure_percen)\n",
    "print(failure_percen_list)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "87947d66",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Relative Failures Rate(%)')"
      ]
     },
     "execution_count": 23,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 900x450 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "year_list = ['[0,1)', '[1,2)', '[2,3)', '[3,4)', '[4,5)', '[5,6)']\n",
    "#定义画布尺寸和分辨率\n",
    "plt.figure(figsize=(6, 3), dpi=150)\n",
    "\n",
    "#绘制柱状图\n",
    "x = range(len(year_list))\n",
    "plt.xticks(x, year_list)\n",
    "plt.bar(x, failure_percen_list, width=0.4, color=['#ffaa00' if i>20 else '#0066CC' for i in failure_percen_list])\n",
    "\n",
    "# 柱状图修饰\n",
    "# 添加提示信息\n",
    "plt.title(\"\")  # 添加标题\n",
    "plt.xlabel(\"Age(years)\")  # 添加x轴标签\n",
    "plt.ylabel(\"Relative Failures Rate(%)\")  # 添加y轴标签"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "035833aa",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.7.4"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
